Triple

T10797628
Position Surface form Disambiguated ID Type / Status
Subject Hamburg metropolitan region E254750 entity
Predicate containsUrbanArea P11388 FINISHED
Object Ahrensburg E249427 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Ahrensburg | Statement: [Hamburg metropolitan region, containsUrbanArea, Ahrensburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ahrensburg
Context triple: [Hamburg metropolitan region, containsUrbanArea, Ahrensburg]
  • A. Ahrensburg chosen
    Ahrensburg is a town in northern Germany’s Schleswig-Holstein state, known for its historic castle and proximity to Hamburg.
  • B. Ehringshausen
    Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
  • C. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • D. Albershausen
    Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern Germany.
  • E. Augustdorf
    Augustdorf is a municipality in North Rhine-Westphalia, Germany, known for its proximity to the Teutoburg Forest and its significant military presence, including Bundeswehr facilities.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d73333dc4081909faa40c10bce2735 completed April 9, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2d6a7b8c481908249acfffc97b08a completed April 18, 2026, 12:56 a.m.
Created at: April 8, 2026, 9:17 p.m.